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[0001] The present invention relates generally to improved techniques for location determination. More particularly, the invention relates to techniques for determining the location of a portable device based on signals received from radio frequency tags dispersed throughout a region within which the location of the portable device is to be determined.
[0002] Identifying the location of a wireless telephone making an emergency call is a matter of great importance and intense interest. Unlike a landline telephone whose telephone number is associated with a particular address, wireless telephones can be used from anywhere and knowledge of the telephone number of the wireless telephone making the call contributes nothing toward the knowledge of the location of the wireless telephone at the time of the call.
[0003] Global positioning system (GPS) receivers have great value in determining the location of a user, but the use of a GPS receiver is not a reliable solution for determining the location of a user inside a building, especially in a critical application such as providing a user's location to an emergency call system. It is possible for a receiver with a good view of the sky to determine its position. Such a receiver can be integrated into a wireless telephone and a wireless telephone from which an emergency call is made can transmit location information provided by the GPS receiver to the central exchange, which in turn can relay this information to emergency personnel. However, even in outdoor use, GPS has drawbacks that detract from its desirability as the sole source of location information in critical situations. A GPS receiver may take several minutes to achieve a location fix, particularly if the GPS receiver has been transported more than a few hundred miles since last achieving a location fix. Moreover, if obstructions prevent the receiver from achieving a clear line of sight to a sufficient number of satellites, the receiver may fail to achieve a fix.
[0004] The use of a GPS receiver within a building introduces even more difficulties. Most building materials obstruct the receiver's view of satellites, preventing the receiver from achieving a location fix. In addition, the accurate determination of a location within a building requires altitude information as well as latitude and longitude information, and the altitude information provided by a GPS is much less accurate than the latitude and longitude information provided. GPS should not be relied on to determine location in a building in critical applications such as location identification of a telephone making an emergency call.
[0005] There exists, therefore, a need for location identification systems and methods that will operate within a building.
[0006] A system of wireless telephone location identification according to an aspect of the present invention employs a set of radio frequency (RF) tags dispersed throughout a building. The location of a wireless telephone or other device employing the tags for location identification is determined by receiving and processing signals received from tags in the vicinity of the device. If signals from multiple tags are employed in determining the location, it is possible to uniquely identify a location even if two or more tags from which signals are received are not unique. The reason for this is that the combination of signals received is highly likely to be unique, even if two or more individual tags are not unique. The tags may be mapped to their locations by moving through the building, suitably once per year, with a device that records signals from the tags and associates each signal with the building and room number in which the tag is located. The mapping information obtained by this procedure can be stored in a location server associated with the building or with an emergency call, or 911, network. Depending on the particular tags employed, the mapping may be accomplished by associating locations with location vectors comprising characteristics of a number of symbols, by associating locations with specific codes or by associating locations with possible paths that may be used to reach the locations.
[0007] When a wireless telephone user makes a 911 call, his or her telephone may receive signals from nearby tags and then transmit the signals to the location server. Alternatively, depending on the tag configuration employed, the telephone may retrieve stored signal information previously received. The location server processes the signals to determine the location of the wireless telephone. If the location server is a part of the 911 network, it sends the location information along with the wireless call. Otherwise, the location server returns the location information to the telephone for transmission to the 911 network.
[0008] A more complete understanding of the present invention, as well as further features and advantages of the invention, will be apparent from the following Detailed Description and the accompanying drawings.
[0009]
[0010]
[0011]
[0012]
[0013]
[0014]
[0015] The user's location is determined by a portable device such as a wireless telephone
[0016] The location server
[0017] A variety of different choices may be made for the tags
[0018] In one exemplary embodiment, the tags
[0019] In order to identify its location, the telephone
[0020] Upon receiving a query from the telephone
[0021] The location identification performed by the location server
[0022] Every time slot should be ignored by one set or another, so that any defective tag would be ignored at least once. The comparison would be repeated a predetermined number of times, with a different set of time slots being ignored each time, and the most probable location of the telephone
[0023] As a further technique to compensate for failure of one or more tags, the wireless telephone
[0024] If desired, a transmitter similar to the transmitter
[0025] In addition to using mapping of the tags to define locations, it is possible to define a more general region by correlating codes from nearby tags. For example, each of the tags
[0026] As an alternative to using tags that simply broadcast specific information in order to distinguish them from one another, it is also possible to use tags that transmit signals with information specifically identifying their location.
[0027] As an alternative to simply broadcasting information distinguishing tags from one another, the tags
[0028] As a further alternative to the above embodiment, a system may be designed having a hierarchy of transmitters, each identifying a smaller region. A building may, for example, have four classes of tags, with a first broadest class identifying the building, the second class identifying the floor, a third class identifying a section of a floor and the fourth most specific class identifying a room. A wireless telephone would receive signals from each class of tag within which it was in range, analyze the signals to identify the tag identifying the region in which the wireless telephone was located and identify the specific location of the tag using the combined information provided by the tags.
[0029]
[0030]
[0031] A system similar to the system
[0032] that is, 120, configurations, and could then distinguish up to 120 individual rooms. In most applications, some of the information provided by the tags would be used for error correction, so that fewer different rooms could be distinguished. In such a system, a wireless telephone or other portable device would transmit an interrogation signal and each tag within range would respond to the interrogation signal with a response signal employing the characteristic resonance of the tag. The portable device would collect the information provided by the response signals, and this information would be used to compute the location of the portable device.
[0033] If desired, the passive resonators to be used may be programmed with random numbers during fabrication. Such tags might be built into normal construction materials, such as floor or ceiling tiles. Typically, one of every 3 to 300 floor or ceiling tiles would contain a tag. The proportion of ceiling tiles containing tags would be determined based on factors such as the range from which emissions from the tags could be detected. In a building with offices of 100 square feet, one tag would typically be provided for every 10 to 50 square feet of floor space. Each tag would typically have between 1 and 6 resonances, with preferably two or more. The resonances would be spaced among 10 or more distinguishable frequencies. Another attractive alternative would be to place passive resonators providing location information into room identification tags typically located by the doorways of rooms in office buildings.
[0034] Passive resonators are typically not able to provide as much information as are active transmitters. In order to overcome the limitations on the information available from a single passive resonator, a system of tags according to an alternative aspect of the present invention employs passive resonators which, in combination, are able to provide a considerable number of bits of information, even though the number of bits provided by any single tag is relatively small.
[0035]
[0036] The tags
[0037] If each tag carries only two bits of useful information, the tags
[0038] The tags
[0039] As a user carries a wireless telephone
[0040] When the telephone
[0041]
[0042] At step
[0043] Experimental results employing active transmitters as illustrated in
[0044] The experimental assumptions were that each telephone records power received during N allocated time slots. After averaging for noise reduction, the power levels P(t) at each of the N time slots where t=1, . . . , N, are taken as a feature vector. These feature vectors are used to predict the identification of the transmitter closest to the telephone at the time when the power is recorded.
[0045] Two test buildings A and B are assumed. Both buildings consist of 10 floors. Each floor is of the same configuration with 10 hallways. Building A has 10 rooms along each hallway and building B has 100 rooms along each of its hallways. All rooms are of the same shape and size, that is, 4×4×4 cubic meters. Building A has a total of 1,000 rooms and building B has a total of 10,000 rooms. It is also possible to identify locations as being in open space or hallways, but for the purposes of conducting a simulation to determine the effectiveness of location identification it is not necessary to distinguish such spaces from ordinary rooms.
[0046] The goal of classification is to identify a particular room where a wireless telephone or other device is located, that is, to discriminate between 1,000 or 10,000 classes, respectively, using feature vectors.
[0047] The identification of a room is coded as a bit vector that is transmitted by tags configured as active transmitters or alternatively echoed by the tags configured as passive resonators or reflectors. For active transmitters, such as those described in
[0048] The bit vector representing each room is generated at installation and is fixed. This vector can be either a random bit pattern or can be derived from a specific coding scheme. In the experiments described here, it was assumed that the first 24 bits of the vector stored the binary representation of the floor/hallway/room number triplet, with 8 bits for each number. The vector was then padded with more bits each having a 50% probability of being on or off, up to a desired length. It was assumed that the active transmitters use 50 bits and the passive resonators use 48 bits.
[0049] Random samples of the location of the wireless telephone were generated following a uniform distribution within the building's space. With each sample, the power received from all nearby transmitters or reflectors was computed according to a signal decay law. With active transmitters, power received at time slot t is
[0050] where P
[0051] With passive reflectors, the power received at each time slot t is
[0052] The vector of received power was computed for each sampled location. The resultant vectors were divided into training and testing sets with no overlap. Two statistical classifiers were compared, the nearest neighbor classifier (nn) and the decision forest classifier (dfc).
[0053] Nearest neighbor classifiers work by comparing each test vector to all training vectors and finding the training vector closest to the test vector according to a chosen metric. The test vector is then assigned the class of the closest training vector, in this case, the location associated with the closest training vector. In the experiment the metric is chosen to be Euclidean distance.
[0054] Decision forest classifiers are voting combinations of several decision trees. Each tree is constructed using training vectors projected to a randomly chosen subspace of the feature space. Each tree decides by matching the test vector to the splitting function at each internal node until a leaf node is reached. The tree then assigns the class or classes at that leaf to the test vector. After all trees have decided, a test vector is assigned to the class receiving the greatest number of votes.
[0055] In the experiments, nine trees were constructed in each forest. Training sets of different sizes are used for each building to scale for different numbers of rooms.
[0056] The accuracy of room identification for each of the hypothetical buildings was as follows. The entries represent the rates of assigning a test location to the correct room. The first percentage value in each entry was the probability of assigning a test location to the correct room, while the second percentage value in each entry, that is, the percentage value in parentheses, was the probability of assigning a test location to either the correct room or the room immediately next to it.
Classifier (training set size) Building A (1000 rooms) Building B (10000 rooms) Active devices nn (#rooms * 10) 58.50% (98.66%) 59.22% (98.53%) dfc (#rooms * 10) 63.82% (99.53%) 66.13% (99.11%) nn (#rooms * 50) 72.03% (99.65%) 72.32% (99.59%) dfc (#rooms * 50) 80.25% (99.97% 81.70% (99.94%) Passive devices nn (#rooms * 10) 50.56% (94.07%) 49.88% (93.05%) dfc (#rooms * 10) 58.99% (95.73%) 57.68% (94.41%) nn (#rooms * 50) 62.43% (98.80%) 62.32% (98.49%) dfc (#rooms * 50) 73.61% (99.17%) 74.17% (98.83%)
[0057] From the experimental results it can be observed that with either type of device it is possible to isolate the location as being one of two adjacent rooms to over 90% accuracy. Generally, the decision forest classifier is preferred over nearest neighbors. The accuracy depends on the training set size, that is, how many power vector samples are collected from each room at installation. The results show that 50 samples will provide very useful performance. The building size, on the other hand, does not seem to matter, since beyond a fixed distance, for example, 30 meters the signal is invisible so the existence of other rooms beyond such an immediate neighborhood does not greatly affect the results. It is expected with longer feature vectors, larger training sets, or more precise power measurements, accuracy can be further improved.
[0058] Results in actual buildings should be better than those presented here, because the absorption of RF signals in real buildings is concentrated in walls and floors. In the simulations described above, absorption has been assumed to be spread uniformly throughout the rooms.
[0059] While the present invention is disclosed in the context of a presently preferred embodiment, it will be recognized that a wide variety of implementations may be employed by persons of ordinary skill in the art consistent with the above discussion and the claims which follow below. In particular, it will be recognized that the use of the invention is not limited to the identification of a location for use with an emergency call system or with any call system, but may easily be adapted to identify a user's location for the benefit of the user.
[0060] For example, a user may employ a suitable portable device that computes his or her location and then informs the user of his or her location, for example by audibly telling the user the floor and room number. Such a system would be particularly useful to visually impaired users, or for any other user who desire help in navigating through a building, for example persons who are unfamiliar with the layout of the building. Further, the location system could be readily adapted to a security system in which it is desirable to track the movements of all entrants to a secure building or higher security area within a building.